Algorithmic bias: review, synthesis, and future research directions

As firms are moving towards data-driven decision making, they are facing an emerging problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially-biased outcomes, thereby compounding inequalities in the workplace and in society. This paper reviews, summarises, and synthesises the current literature related to algorithmic bias and makes recommendations for future information systems research. Our literature analysis shows that most studies have conceptually discussed the ethical, legal, and design implications of algorithmic bias, whereas only a limited number have empirically examined them. Moreover, the mechanisms through which technology-driven biases translate into decisions and behaviours have been largely overlooked. Based on the reviewed papers and drawing on theories such as the stimulus-organism-response theory and organisational justice theory, we identify and explicate eight important theoretical concepts and develop a research model depicting the relations between those concepts. The model proposes that algorithmic bias can affect fairness perceptions and technology-related behaviours such as machine-generated recommendation acceptance, algorithm appreciation, and system adoption. The model also proposes that contextual dimensions (i.e., individual, task, technology, organisational, and environmental) can influence the perceptual and behavioural manifestations of algorithmic bias. These propositions highlight the significant gap in the literature and provide a roadmap for future studies.

Automated summary

This paper reviews and synthesizes current literature on algorithmic bias, pointing out a lack of empirical research and a neglect of the mechanisms through which biased algorithms influence decisions and behaviors. It identifies eight important theoretical concepts and proposes a research model depicting the relationships between these concepts, highlighting the impact of algorithmic bias on fairness perceptions and technology-related behaviors. The model also suggests that contextual dimensions play a crucial role in shaping perceptions and behaviors related to algorithmic bias.